model = dict(
    type='RetinaNet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32),
    backbone=dict(
        type='ResNet',
        depth=18,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=None), #　dict(type='Pretrained', checkpoint='torchvision://resnet18')
    neck=dict(
        type='FPN',
        in_channels=[64, 128, 256, 512],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5),
    bbox_head=dict(
        type='RetinaHead',
        num_classes=8,  # 80
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0,
            ignore_iof_thr=-1),
        sampler=dict(type='PseudoSampler'),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))
dataset_type = 'CocoDataset'
# data_root = 'data/coco/'
data_root = ''
# dataset_type = 'CustomDataset'
file_client_args = dict(backend='disk')
# train_pipeline = [
#     dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
#     dict(type='LoadAnnotations', with_bbox=True),
#     dict(type='Resize', scale=(1333, 800), keep_ratio=True),
#     dict(type='RandomFlip', prob=0.5),
#     dict(type='PackDetInputs')
# ]
# test_pipeline = [
#     dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
#     dict(type='Resize', scale=(1333, 800), keep_ratio=True),
#     dict(type='LoadAnnotations', with_bbox=True),
#     dict(
#         type='PackDetInputs',
#         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
#                    'scale_factor'))
# ]
train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type='CustomDataset',
        # type = 'CocoDataset',
        data_root='',  #　data/coco/
        ann_file='data/kitti_tiny/train_ann.pkl',
        data_prefix=dict(img="data/kitti_tiny/training/image_2/"),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PackDetInputs')
        ]))
val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CustomDataset',
        # type = 'CocoDataset',
        data_root='',  # data/coco/
        ann_file='data/kitti_tiny/val_ann.pkl',
        data_prefix=dict(img="data/kitti_tiny/training/image_2/"),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
test_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CustomDataset',
        # type = 'CocoDataset',
        data_root='',  # data/coco/
        ann_file='data/kitti_tiny/val_ann.pkl',
        data_prefix=dict(img="data/kitti_tiny/training/image_2/"),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))




val_evaluator = dict(
    type='CocoMetric',
    ann_file='data/kitti_tiny/val_ann.pkl',
    metric='bbox',
    format_only=False)
test_evaluator = dict(
    type='CocoMetric',
    ann_file='data/kitti_tiny/val_ann.pkl',
    metric='bbox',
    format_only=False)

evaluation = dict(interval=3, metric='mAP')
# optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001) ##########
# optimizer_config = dict(grad_clip=None)
# lr_config = dict(
#     policy='step',
#     warmup='linear',          ##############
#     # warmup=None,
#     warmup_iters=50,    ##################3
#     warmup_ratio=0.001,
#     step=[8, 11])

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=15, val_interval=3)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=12,
        by_epoch=True,
        milestones=[8, 11],
        gamma=0.1)
]

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001))
auto_scale_lr = dict(enable=False, base_batch_size=16)
default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=3),  ########log_confg
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=3),          ########checkpoint_config.interval
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))

# log_config = dict(interval=5, hooks=[dict(type='TextLoggerHook')])
# checkpoint_config = dict(interval=12)

env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = 'retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth' #  None
resume = False
